Exploring the complex interrelation between depressive symptoms, risk, and protective factors: A comprehensive network approach.

Autor: Iovoli F; Osnabrueck University, Germany. Electronic address: flavio.iovoli@uos.de., Hall M; Osnabrueck University, Germany., Nenadic I; Philipps University of Marburg, Germany., Straube B; Philipps University of Marburg, Germany., Alexander N; Philipps University of Marburg, Germany., Jamalabadi H; Philipps University of Marburg, Germany., Jansen A; Philipps University of Marburg, Germany., Stein F; Philipps University of Marburg, Germany., Brosch K; Philipps University of Marburg, Germany., Thomas-Odenthal F; Philipps University of Marburg, Germany., Usemann P; Philipps University of Marburg, Germany., Teutenberg L; Philipps University of Marburg, Germany., Wroblewski A; Philipps University of Marburg, Germany., Pfarr J; Philipps University of Marburg, Germany., Thiel K; University of Münster, Germany., Flinkenflügel K; University of Münster, Germany., Meinert S; University of Münster, Germany., Grotegerd D; University of Münster, Germany., Hahn T; University of Münster, Germany., Goltermann J; University of Münster, Germany., Gruber M; University of Münster, Germany., Repple J; University of Münster, Germany., Enneking V; University of Münster, Germany., Winter A; University of Münster, Germany., Dannlowski U; University of Münster, Germany., Kircher T; Philipps University of Marburg, Germany., Rubel JA; Osnabrueck University, Germany.
Jazyk: angličtina
Zdroj: Journal of affective disorders [J Affect Disord] 2024 Jun 15; Vol. 355, pp. 12-21. Date of Electronic Publication: 2024 Mar 26.
DOI: 10.1016/j.jad.2024.03.119
Abstrakt: Background: Depressive symptoms seem to be interrelated in a complex and self-reinforcing way. To gain a better understanding of this complexity, the inclusion of theoretically relevant constructs (such as risk and protective factors) offers a comprehensive view into the complex mechanisms underlying depression.
Methods: Cross-sectional data from individuals diagnosed with a major depressive disorder (N = 986) and healthy controls (N = 1049) were analyzed. Participants self-reported their depressive symptoms, as well as several risk factors and protective factors. Regularized partial correlation networks were estimated for each group and compared using a network comparison test.
Results: Symptoms of depression were more strongly connected in the network of depressed patients than in healthy controls. Among the risk factors, perceived stress, the experience of negative life events, emotional neglect, and emotional abuse were the most centrally embedded in both networks. However, the centrality of risk factors did not significantly differ between the two groups. Among the protective factors, social support, personal competence, and acceptance were the most central in both networks, where the latter was significantly more strongly associated with the symptom of self-hate in depressed patients.
Conclusion: The network analysis revealed that key symptoms of depression were more strongly connected for depressed patients than for healthy controls, and that risk and protective factors play an important role, particularly perceived stress in both groups and an accepting attitude for depressed patients. However, the purpose of this study is hypothesis generating and assisting in the potential selection of non-symptom nodes for future research.
Competing Interests: Declaration of competing interest We report no conflicts of interest.
(Copyright © 2024 The Authors. Published by Elsevier B.V. All rights reserved.)
Databáze: MEDLINE
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